The goal of hydrocarbon exploration is to find porous and permeable geologic deposits containing high pore-space saturations of hydrocarbons, under sufficient pressure to allow some mode of commercial production. In pursuit of this goal, companies, countries and individuals collect and process many types of geophysical and geological data. The data is often analyzed to find anomalous zones that can reasonably be attributed to the presence of hydrocarbons.
The usage of 2D and 3D seismic data anomalies has been a standard practice in the petroleum industry since the 1960s. Other geologic and geophysical data anomalies have been tried, sometimes successfully, for over a century. These include various gravimetric, electromagnetic, chemical, biological and speculative methods.
The usage of anomalies for oil and gas detection has been plagued by several problems. First, most remote sensing anomalies (e.g., a 3D seismic amplitude anomaly) cannot be directly tied to a rock property that could be measured in the laboratory or using well logs. Much effort is expended attempting to tie observed anomalies to known rock responses by modeling the expected attribute response or otherwise correlating with a known producing reservoir. This work is often based on the experience of the practitioner.
A second problem is that the anomalies themselves are often evaluated or tied to response models in a qualitative manner. With qualitative assessment as the basis, quantitative, objective and reproducible error analysis has not been possible.
A third problem is that a basic physical property at work in hydrocarbon reservoirs is that both oil and gas are less dense than water. This generally causes oil and gas to accumulate up-structure in the pore-space of potential reservoir rocks. The higher water saturations are found, generally, down-structure. The exception to this is the case of heavy oil which may have a density greater than that of water. In the case of heavy oil, water may accumulate up-structure. Generally, the separation of saturations is driven by gravity. When such a separation of fluid types occurs, flat interfaces, in depth, are expected to form.
This separation causes numerous possible classes of data attribute response. First, the hydrocarbon reservoir will have one response for each hydrocarbon type. The water-saturated part of the reservoir may have a second data response and the interfacial area a third type of attribute data response.
The present invention is designed for the detection, quantification and evaluation of the depth and location of interface between lighter and heavier saturating fluids as exhibited in a data attribute dataset to locate the interface between a water reservoir and a hydrocarbon reservoir.
The lack of quantification, error analysis, subjectivity of analysis and data quality issues cause variations in the appraisal of data anomalies in oil and gas exploration and production projects. It is not uncommon for different individuals or companies to examine the same anomaly and reach irreconcilably, different conclusions. In many cases, explaining quantitatively why the anomaly of one prospect should be “believed or trusted” more than that of another prospect has not been possible. This lack of trust causes different entities to make drastically different investment decisions concerning prospects based on the same underlying data.
The present embodiments are designed for the detection, quantification and evaluation of data anomalies in the search for producible hydrocarbon deposits. The present embodiments are designed to simultaneously detect, quantify and summarize the interfaced zone between the hydrocarbon reservoir part and the water reservoir part of the data, and between hydrocarbon reservoirs having different fluid densities or saturations. The embodiments address the case of multiple hydrocarbon zones, e.g., gas over oil over water. The embodiments are designed to test the model wherein gas is less dense than oil and oil is less dense than water and the case of heavy oil being more dense than water or than gas, with data responses varying by structural position.
The current embodiments are designed to function in areas of low signal-to-noise and aid in the determination of data suitability for hydrocarbon detection for the expected rock physics environment. The current embodiment, therefore, can be applied to the detection of subtle hydrocarbon related data anomalies.
A need exists for a method to scan large amounts of geophysical data sets systematically and simultaneously to find the presence of hydrocarbons. The method should honor non-statistical and highly structured (due to geology and rock properties) host rock geophysical responses. The method should honor small changes in the host rock layering or composition in constructing background data volumes for normalization and scanning.
The present embodiments meet these needs.
The present embodiments are detailed below with reference to the listed Figures.